Computer systems typically include a combination of hardware (e.g., semiconductors, circuit boards, etc.) and software (e.g., computer programs). One use of computer systems is for simulations of real-world activity. An important class of simulation is that used for training. Users often train for real-world activity using simulators because using the simulation is less expensive, more efficient, or less dangerous than training using the actual real-world activity.
An important design objective of current simulations is often realism, meaning that the simulation portrays, reflects, implements, or simulates actual real-world activity or events as closely as possible. But, in a training situation, complete simulation realism can actually result in ambiguous or undesirable positive and negative feedback for the user trainee. This is true, for example, when the user makes an “incorrect” decision (i.e., an error), but the parameters of the simulation (especially random/probabilistic effects or effects not under the user's direct control) allow the outcome that the user experiences to be positive, in spite of the user's error. To understand this phenomenon, consider the example of a flight simulator designed to train user pilots to land an airplane. One of the actions on a landing checklist, which the user trainee is to follow, could be the application of carburetor heat, as a precautionary measure to prevent the possible formation of ice in the carburetor, which could result in loss of engine power. But, the formation of ice in a carburetor does not always occur in the absence of carburetor heat, depending on a variety of factors, such as the ambient temperature, the relative humidity, and the velocity of air and fuel through the carburetor. Thus, a flight simulator that simulates the probability of the occurrence of real world events in a completely realistic manner will simulate a loss of engine power only occasionally, in response to the user error of failing to follow the landing checklist. Thus, a simulation that simulates the probability of real-world events in a completely realistic manner is not necessarily the best tool for learning because in a completely realistic simulation, sometimes users make mistakes and suffer no adverse consequences. Conversely, sometimes adverse events occur that are beyond the control of the user. For example, a pilot may encounter adverse weather conditions that were unforeseeable and unavoidable. But, an inexperienced user (who is precisely the type of user who is likely to be training using the simulator) may experience difficulty in distinguishing between negative feedback that was preventable and negative feedback that was unavoidable, which may cause confusion and lack of confidence.
Current simulators have attempted to address the aforementioned problems via the following techniques. As a first technique, some simulators give warning messages in response to user errors or information messages when unpreventable negative feedback occurs. For example, a simulator might display a warning error message of the type: “You forgot to apply carburetor heat” or an informational message of the type “A wind shear event occurred, but there was nothing you could have done to prevent it.” While such techniques do provide the user with negative feedback or reassurance in real-time, the realism of the simulation is distorted in that the warning or informational message is artificial, meaning that such a warning or message would not occur if the user were actually flying the airplane. Further, the user does not experience the potential adverse effects of the error, such as the simulated loss of engine power, which would be more memorable than the mere warning message. Also, the user may become dependent upon the artificial warning or message, and when confronted with the real-world event, the absence of a warning message might be interpreted as confirmation that all is well (when, in fact, all is not well), and the absence of the reassuring information message might be interpreted as an indication that the negative feedback was avoidable (when, in fact, the negative feedback was unavoidable).
As a second technique, some simulators are designed with the assumption that the training repetitions will be sufficient, so that the negative outcome (e.g., the simulated loss of engine power) will occur often enough to provide useful (for training purposes) negative feedback. The problems with relying on training repetitions are 1) the negative outcome might be sufficiently rare so to be negligible, even with a large number of training repetitions, and 2) the probability of a negative outcome might be close to the probability of a positive outcome, so that the difference between making an error and performing correctly is nearly imperceptible to the user.
As a third technique, some simulators rely on an after-the-fact debriefing or feedback by a human instructor to point out errors made by the user or provide reassurance to the user. Unfortunately, the positive feedback that the user receives (landing the plane successfully despite the error of failing to apply carburetor heat) still occurs in real-time, and this positive feedback may be too influential in reinforcing the incorrect behavior/decision. Also, a human instructor might not be available for every simulation or might not notice every error or unpreventable negative feedback, the likelihood of which increases if the instructor is distracted by supervising multiple user trainees.
Thus, without a better way to simulate real-world events, users will not receive the full benefit of learning from training simulators.